By Zheng-Hua Tan BSc, MSc, PhD, Børge Lindberg MSc (auth.)
The notable advances in computing and networking have sparked a big curiosity in deploying automated speech attractiveness on cellular units and over conversation networks. This development is accelerating.
This ebook brings jointly major educational researchers and commercial practitioners to deal with the problems during this rising realm and provides the reader with a entire advent to the topic of speech acceptance in units and networks. It covers community, dispensed and embedded speech popularity platforms, that are anticipated to co-exist sooner or later. It deals a wide-ranging, unified method of the subject and its most recent improvement, additionally protecting the main up to date criteria and a number of other off-the-shelf systems.
• offers an in-depth assessment of community speech attractiveness, allotted speech popularity, embedded speech acceptance, structures and applications
• starts with a complete assessment of the topic, discussing the professionals and cons of the provided techniques, and guiding the reader during the following chapters
• contains systems like cell phones, PDAs and automobiles
• offers state of the art tools, complex platforms, and the most recent standards
• bargains operating wisdom wanted for either learn and practice
• References supplemental fabric at linked complementary web site at: http://asr.es.aau.dk
This all-inclusive text/reference is a necessary learn for graduate scholars, scientists and engineers operating or learning within the box of speech attractiveness and processing. It bargains a self-contained method of this scorching study topic.
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Additional resources for Automatic Speech Recognition on Mobile Devices and over Communication Networks
It is thus impossible to present a detailed view of all of them in this chapter. org). As a consequence, we decided to present, in this section, only the coders that are used in the experiments further described in this chapter. Theses coders are nevertheless widely used in different applications: GSM (used in European mobile wireless Speech Coding and Packet Loss Effects on Speech and Speaker Recognition 29 communication), G711 and G723 (used in some VoIP protocols) and MPEG (used for audio compression).
VSR uses the properties of the LDA, discriminative training and HMM parameter coding. Discriminative training is used to achieve high recognition rate with a moderate amount of Gaussians. Here a performance measure like the minimum word error (MWE) is applied for training. After Viterbi based maximum likelihood training, 10 iterations of MWE based training were performed. HMM parameters are coded using Subspace Distribution Clustering HMMs (SDCHMMs) where the Gaussians are represented by pointers to a codebook.
The advantage is that no explicit detection of non-speech segments is needed. For every frequency bin the noise estimate is subtracted from the noisy speech signal where flooring is employed. 6 Discussion This chapter presented an extensive overview on speech recognition on mobile devices and over communication networks. We analyzed the system architecture and requirements of speech recognition, the resource situation and constraints on various targets like mobile devices and networks, and presented the characteristics of three main solutions in detail: network speech recognition, distributed speech recognition and embedded speech recognition.
Automatic Speech Recognition on Mobile Devices and over Communication Networks by Zheng-Hua Tan BSc, MSc, PhD, Børge Lindberg MSc (auth.)